Results 11 - 20 of 247,386

Efficient and Effective Clustering Methods for Spatial Data Mining

by Raymond T. Ng, Jiawei Han , 1994
"... Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLARANS which ..."
Abstract - Cited by 709 (37 self) - Add to MetaCart
Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLARANS which

PAUP*: Phylogenetic Analysis Using Parsimony (and other methods) 4.0.b5

by David L. Swofford , 2001
"... This documentation is currently under construction, therefore, we cannot guarantee the accuracy of the information described herein. A much more user-friendly manual is in progress. Revisions will be made available to registered users over the World Wide Web at ..."
Abstract - Cited by 5090 (6 self) - Add to MetaCart
This documentation is currently under construction, therefore, we cannot guarantee the accuracy of the information described herein. A much more user-friendly manual is in progress. Revisions will be made available to registered users over the World Wide Web at

On Spectral Clustering: Analysis and an algorithm

by Andrew Y. Ng, Michael I. Jordan, Yair Weiss - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS , 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract - Cited by 1713 (13 self) - Add to MetaCart
Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors

Sequential Monte Carlo Methods for Dynamic Systems

by Jun S. Liu, Rong Chen - Journal of the American Statistical Association , 1998
"... A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ..."
Abstract - Cited by 664 (13 self) - Add to MetaCart
ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We deliver a guideline on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic

Opinion Mining and Sentiment Analysis

by Bo Pang, Lillian Lee , 2008
"... An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, active ..."
Abstract - Cited by 749 (3 self) - Add to MetaCart
, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part

Three approaches to qualitative content analysis.

by Hsiu-Fang Hsieh , Sarah E Shannon , 2005
"... Content analysis is a widely used qualitative Researchers regard content analysis as a flexible method for analyzing text data The differentiation of content analysis is usually limited to classifying it as primarily a qualitative versus quantitative research method. A more thorough analysis of th ..."
Abstract - Cited by 729 (0 self) - Add to MetaCart
Content analysis is a widely used qualitative Researchers regard content analysis as a flexible method for analyzing text data The differentiation of content analysis is usually limited to classifying it as primarily a qualitative versus quantitative research method. A more thorough analysis

Comprehensive database for facial expression analysis

by Takeo Kanade, Jeffrey F. Cohn, Yingli Tian - in Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition
"... Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, ..."
Abstract - Cited by 593 (51 self) - Add to MetaCart
Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis

Bayesian Analysis of Stochastic Volatility Models

by Eric Jacquier, Nicholas G. Polson, Peter E. Rossi , 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized ARCH ..."
Abstract - Cited by 601 (26 self) - Add to MetaCart
this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized

Model-Based Clustering, Discriminant Analysis, and Density Estimation

by Chris Fraley, Adrian E. Raftery - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
Abstract - Cited by 573 (29 self) - Add to MetaCart
Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However

Nonlinear component analysis as a kernel eigenvalue problem

by Bernhard Schölkopf, Alexander Smola, Klaus-Robert Müller - , 1996
"... We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all ..."
Abstract - Cited by 1573 (83 self) - Add to MetaCart
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all
Results 11 - 20 of 247,386